Mobile device localization using audio signals

ABSTRACT

Mobile device localization using audio signals is described. In an example, a mobile device is localized by receiving a first audio signal captured by a microphone located at the mobile device and a second audio signal captured from a further microphone. A correlation value between the first audio signal and second audio signal is computed, and this is used to determine whether the mobile device is in proximity to the further microphone. In one example, the mobile device can receive the audio signals from the further microphone and calculate the correlation value. In another example, a server can receive the audio signals from the mobile device and the further microphone and calculate the correlation value. In examples, the further microphone can be a fixed microphone at a predetermined location, or the further microphone can be a microphone located in another mobile device.

BACKGROUND

Positioning systems and techniques enable the location of devices to bedetermined and utilized to provide useful services. For example, theglobal positioning system (GPS) uses signals from a constellation ofsatellites to localize a receiver to within a few tens of meters.However, whilst systems such as GPS work effectively in open, outdoorenvironments, they typically do not operate well in indoor environmentsdue to a lack of line-of-sight to the satellites.

Whilst alternative positioning techniques, such as those based on cellsite identities can be used indoors, these techniques tend to have alower accuracy than GPS and are more unpredictable due to uneven radiopropagation. As location-based services become more pervasive anduseful, it is therefore beneficial to be able to determine the positionof a mobile device in indoor environments, without the addition ofcomplex or expensive infrastructure or hardware at the mobile device.

The embodiments described below are not limited to implementations whichsolve any or all of the disadvantages of known positioning techniques.

SUMMARY

The following presents a simplified summary of the disclosure in orderto provide a basic understanding to the reader. This summary is not anextensive overview of the disclosure and it does not identifykey/critical elements of the invention or delineate the scope of theinvention. Its sole purpose is to present a selection of conceptsdisclosed herein in a simplified form as a prelude to the more detaileddescription that is presented later.

Mobile device localization using audio signals is described. In anexample, a mobile device is localized by receiving a first audio signalcaptured by a microphone located at the mobile device and a second audiosignal captured from a further microphone. A correlation value betweenthe first audio signal and second audio signal is computed, and this isused to determine whether the mobile device is in proximity to thefurther microphone. In one example, the mobile device can receive theaudio signals from the further microphone and calculate the correlationvalue. In another example, a server can receive the audio signals fromthe mobile device and the further microphone and calculate thecorrelation value. In examples, the further microphone can be a fixedmicrophone at a predetermined location, or the further microphone can bea microphone located in another mobile device.

Many of the attendant features will be more readily appreciated as thesame becomes better understood by reference to the following detaileddescription considered in connection with the accompanying drawings.

DESCRIPTION OF THE DRAWINGS

The present description will be better understood from the followingdetailed description read in light of the accompanying drawings,wherein:

FIG. 1 illustrates a schematic diagram of an indoor positioning systemusing a central server for location calculation relative to fixedmicrophones;

FIG. 2 illustrates a schematic diagram of an indoor positioning systemusing a mobile device for location calculation relative to fixedmicrophones;

FIG. 3 illustrates a schematic diagram of an indoor positioning systemusing a mobile device for location calculation relative to other mobiledevices;

FIG. 4 illustrates a flow chart of a process for determining a locationof a mobile device using audio signals;

FIG. 5 illustrates a functional block diagram of an indoor localizer;and

FIG. 6 illustrates an exemplary computing-based device in whichembodiments of the mobile device localization technique may beimplemented.

Like reference numerals are used to designate like parts in theaccompanying drawings.

DETAILED DESCRIPTION

The detailed description provided below in connection with the appendeddrawings is intended as a description of the present examples and is notintended to represent the only forms in which the present example may beconstructed or utilized. The description sets forth the functions of theexample and the sequence of steps for constructing and operating theexample. However, the same or equivalent functions and sequences may beaccomplished by different examples.

Although the present examples are described and illustrated herein asbeing implemented in a mobile computing system, the system described isprovided as an example and not a limitation. As those skilled in the artwill appreciate, the present examples are suitable for application in avariety of different types of embedded or dedicated systems in whichindoor positioning is useful.

Within most indoor environments there exists low level backgroundacoustic noise, in addition to louder acoustic sounds coming from, forexample, people, music, TVs, etc. These sounds are often unique to acertain physical space. For example, the sounds present in a kitchen maygenerally be different from those in a living room. These sounds cantherefore be utilized as part of a positioning system to determine whichroom of an indoor environment a user is located in.

In order to utilize audio signals to determine the position of a user, amobile device capable of sampling the ambient noise in the area of theuser can be used. Many users already possess such a device in the formof a mobile telephone or other portable computing device (such as alaptop computer or tablet device). These devices generally alreadycomprise microphones, and are able to sample audio signals.

The techniques described below localize a user by comparing audiosignals captured by a mobile microphone associated with the user withaudio signals captured by other microphones (which can be fixed ormobile) in order to determine a relative location of the user to theother microphones. FIGS. 1 to 3 below describe three different examplepositioning systems utilizing this technique, and a method fordetermining location from the audio signals is described with referenceto FIGS. 4 and 5.

Reference is first made to FIG. 1, which illustrates a first exampleindoor positioning system. FIG. 1 shows a schematic diagram of an indoorpositioning system using a central server for location calculationrelative to fixed microphones. The example of FIG. 1 illustrates anindoor environment 100 comprising a first room 102, a second room 104and a third room 106. In other examples a different number of rooms indifferent configurations can be present in the indoor environment.

A mobile device 108 associated with a user comprises a microphone 110.The microphone 110 is able to capture audio from the vicinity of theuser. In the example of FIG. 1, the mobile device 108 is located in thesecond room 104, and is able to capture audio signals from within thisroom. In the example of FIG. 1, the mobile device is a mobile telephone.However, in other examples, the mobile device can be a laptop computer,tablet device, or any other type of mobile computing device. In furtherexamples, the mobile device can be a dedicated device for using audiosignals for localization.

The system of FIG. 1 aims to determine the location of the mobile device108 in terms of which room the mobile device is located in. By proxy,this can also be used to estimate the location of the user, as the useris likely to be in the same room as the mobile device.

Each room of the indoor environment 100 comprises a microphone. Forexample, the first room 102 comprises microphone 112, the second room104 comprises microphone 114, and the third room 106 comprisesmicrophone 116. In this example, these room microphones are fixed, andassociated with predefined locations (e.g. the rooms in which they areplaced). The example of FIG. 1 shows one microphone in each room. Inalternative examples, rooms or spaces in the indoor environment cancomprise more than one microphone. The microphones within the rooms areable to capture ambient noise from within the room. In examples, thiscan comprise both background noise (e.g. appliances, air conditioning,music etc.) as well as foreground noise (e.g. speech).

In one example, one or more of the room microphones may be dedicatedmicrophones placed in the room for the purposes of determining location.In other examples, one or more of the microphones may already be presentin equipment located in the rooms. This may be any fixed device havingaudio capture capabilities. For example, in the case of an indoorenvironment that is an office, each room may have conferencing equipmentor landline telephones present. Such equipment already comprisesmicrophones able to capture audio from the room.

The indoor positioning system of FIG. 1 further comprises a computingdevice 118 such as a server. The computing device is executing localizerfunctionality 120, which is arranged to compare the audio signals fromthe microphone 110 in the mobile device 108 and the further microphones(112, 114, 116), and determine the location of the mobile device 108.More detail on the operation of the localizer functionality is describedwith reference to FIGS. 4 and 5 below.

The computing device 118 receives the audio signal from the mobiledevice 108 via a wireless interface 122. The wireless interface 122 maybe located at or in the computing device 118, or remote from it (e.g.connected over a communications network). The wireless interface 122 isarranged to receive signals transmitted from the mobile device 108.These signals can comprise audio information captured by the microphone110, or data derived therefrom. The user of the mobile device 108 can beprompted to provide consent for the audio signal to be transmitted tothe computing device 118. The audio signal received at the wirelessinterface 122 from the mobile device 108 is provided to the localizerfunctionality 120 at the computing device 118.

In one example, the wireless interface 122 can be in the form of anaccess point, and the wireless interface 122 can communicate with themobile device using any suitable short range communication techniquesuch as WiFi or Bluetooth. In alternative examples, the wirelessinterface 122 can be in the form of base station, and the wirelessinterface 122 can communicate with the mobile device using any suitablecellular communication technique such as GSM, GPRS, UMTS, WiMAX or LTE.

The computing device 118 is connected to the room microphones (112, 114,116) and receives the audio signals from these microphones and providesthem to the localizer functionality 120. The computing device 118 may beconnected to the room microphones (112, 114, 116) directly or via acommunication network. In the example of FIG. 1, the microphones providean analogue signal directly to the computing device 118, and these aresampled as digital data by an analogue-to-digital converter 124 (ADC).The digital representation of the analogue audio signals from themicrophones is then provided to the localizer functionality forprocessing.

In alternative examples, the microphones can each be provided withindividual, local ADCs, such that they each transmit digital audio datato the computing device (either directly or via a network). In furtherexamples, the room microphones can also be wireless, and transmit theaudio signals to the computing device 118 wirelessly (e.g. to thewireless interface 122), rather than using a wired connection.

As the computing device 118 (e.g. server) of FIG. 1 receives the audiosignals and determines a location for the mobile device 108, the examplepositioning system of FIG. 1 represents a centralized architecture. Thesystem of FIG. 1 may therefore be suitable for a controlled environmentsuch as a home or office, where the computing device receiving the audiosignals is maintained locally. In such scenarios the users canexplicitly consent to the capture of ambient audio signals by themicrophones.

Reference is now made to FIG. 2, which illustrates a second examplepositioning system. FIG. 2 shows a schematic diagram of an indoorpositioning system using a mobile device for location calculationrelative to fixed microphones. Unlike the system of FIG. 1, the systemof FIG. 2 does not utilize a central server for location calculation.

FIG. 2 again shows the indoor environment 100 comprising the first room102, the second room 104, the third room 106, and the mobile device 108associated with the user. The mobile device 108 again comprisesmicrophone 110, and the first room 102 comprises microphone 112, thesecond room 104 comprises microphone 114, and the third room 106comprises microphone 116. As with FIG. 1, the room microphones are fixedand are associated with their respective rooms. Note that in otherexamples a different number of rooms or microphones in differentconfigurations can be present.

As before, the mobile device microphone 110 is able to capture ambientaudio from the vicinity of the user, and the room microphones are ableto capture ambient audio from within their rooms. The system of FIG. 2aims to determine the location of the mobile device 108 in terms ofwhich room the mobile device is located in.

Rather than communicating with a central computing device, in theexample of FIG. 2, the room microphones (112, 114, 116) are eachconnected to a transmitter 202, and the transmitter communicates withthe mobile device 108. In this configuration, the room microphones eachtransmit their audio signals directly to the mobile device 108. Forexample, the room microphones may transmit their audio signals using ashort range wireless communication technique such as WiFi or Bluetooth.A corresponding receiver arranged to receive the audio signals from thetransmitters 202 is present at the mobile device 108.

In alternative examples, rather than using a separate transmitter 202for each microphone, the microphones can be connected to a commontransmitter or access point that transmits the audio signals for aplurality of microphones.

In the example of FIG. 2, the mobile device 108 executes the localizerfunctionality 204. The localizer functionality 204 is similar to thatdescribed above with reference to FIG. 1, and is described below in moredetail with reference to FIGS. 4 and 5. The localizer functionality 204receives the audio signal from the microphone 110 in the mobile device108 that describes the ambient noise in the vicinity of the mobiledevice 108. The localizer functionality 204 also receives the audiosignals from each of the room microphones 112, 114, 116 that describethe ambient noise in the vicinity of the each of the rooms of the indoorenvironment 100. The localizer functionality 204 then compares theseaudio signals (as described below) to determine the location of themobile device.

Therefore, the system of FIG. 2 enables the determination of thelocation of the mobile device 108 in the indoor environment (in terms ofwhich room it is located in). This is achieved without the use of acentral server, as the processing for the localization functionality isperformed at the mobile device 108. This means that the audio signalfrom the mobile device microphone 110 is not transmitted outside themobile device 108, which may be useful in scenarios that are not underthe control of the user (e.g. not in a home/office environment),although user consent can again be obtained prior to audio capture.

Reference is now made to FIG. 3, which illustrates a third examplepositioning system. FIG. 3 shows a schematic diagram of an indoorpositioning system that calculates location relative to other mobiledevices. The system of FIG. 3 differs from those in FIG. 1 or 2 in thatthe system is not aiming to determine the location of the mobile device108 in terms of which room of an indoor environment the mobile device isin, but rather the system aims to determine which other mobile devicesit is in proximity to.

The use of acoustic signals for this purpose enables a morerepresentative location to be determined for indoor environments. Forexample, low power radio signals can be transmitted between mobiledevices to ascertain whether they are in proximity. However, thesesignals pass readily through walls, floors, windows, and other internalstructures in indoor environments. Therefore, when radio signals areused it may appear that certain mobile devices are in proximity, whereasthey are actually in different rooms or on different floors. Thisresults in a difference between what the user perceives as being othermobile devices in proximity, and what the positioning system determines.Acoustic signals are more readily attenuated by indoor structures, andare therefore suitable for determining proximity between mobile devicesthat matches the user's perception.

FIG. 3 again shows the indoor environment 100 comprising the first room102, the second room 104, the third room 106, and the mobile device 108associated with the user. The mobile device 108 again comprisesmicrophone 110 that is able to capture ambient audio from the vicinityof the user. However, fixed room microphones are not present.

The example of FIG. 3 includes three further mobile devices: a firstfurther mobile device 302 comprising microphone 304; a second furthermobile device 306 comprising microphone 308; and a third further mobiledevice 310 comprising microphone 312. Each of these further mobiledevices 302, 306, 310 can communicate with the mobile device 108 of theuser. For example, the further mobile devices 302, 306, 310 maycommunicate with the mobile device 108 using a short range wirelesscommunication technology such as WiFi, Bluetooth or similar. The furthermobile devices 302, 306, 310 transmit audio signals from theirrespective microphones 304, 308, 312 to the mobile device 108, at theconsent of the associated user. These audio signals contain informationon the ambient audio in the vicinity of the associated further mobiledevice.

In the FIG. 3 example, the mobile device 108 executes localizerfunctionality 314. The localizer functionality 314 is similar to thatdescribed above with reference to FIGS. 1 and 2 (and described below inmore detail with reference to FIGS. 4 and 5). The localizerfunctionality 314 receives the audio signal from the microphone 110 inthe mobile device 108 that describes the ambient noise in the vicinityof the mobile device 108. The localizer functionality 314 also receivesthe audio signals from each of the microphones 304, 308, 312 thatdescribe the ambient noise in the vicinity of the each of the furthermobile devices 302, 306, 310. The localizer functionality 314 thencompares these audio signals (as described in more below) to determinethe relative location of the mobile device.

In the illustrative example of FIG. 3, the mobile device 108 is presentin the second room 104. Also present in the second room 104 is thesecond further mobile device 306. When the mobile device 108 comparesthe audio signal from its own microphone 110 with that from themicrophone 308 of the second further mobile device 306 it can determinethat these are sufficiently similar to indicate that the mobile device108 is close to (e.g. in the same room as) the second further mobiledevice 306.

Conversely, in the example of FIG. 3, the first further mobile device302 and third further mobile device 310 are located in different roomsto the mobile device 108. They are still sufficiently close to themobile device 108 that the radio signals comprising the audio from thefirst further mobile device 302 and third further mobile device 310 arereceived at the mobile device 108. However, because they are located indifferent rooms to the mobile device 108, the audio signals from thefirst further mobile device 302 and third further mobile device 310 aredifferent from the audio captured by the mobile device microphone 110.The localizer functionality 314 therefore does not consider the mobiledevice 108 to be in close proximity to the first further mobile device302 and third further mobile device 310.

This matches the user's perception of the relative spatial locations ofthe mobile devices. The user perceives that the second further mobiledevice 306 is close by, as it is in the same room, but does not considerthe first further mobile device 302 or third further mobile device 310to be close as they are in a different room and cannot be seen (despitethe fact that they may be spatially nearby).

The system of FIG. 3 does not provide an absolute location for themobile device 108 in an indoor environment, but does provide a relativelocation between mobile devices. In other words, the positioning systemof FIG. 3 determines which mobile devices are in sufficiently closeproximity to experience similar ambient noises (e.g. in the same room).This relative location information can be used to provide location basedservices such as sharing of documents or presentation materials withparticipants of a meeting that are all in the same room, without sharingwith other mobile devices that are outside the room.

In some examples, to avoid sharing audio signals with unknown mobiledevices, each mobile device can be arranged to only send an audio signalto another mobile device if the user has expressly permitted thecommunication, or if the other mobile device is pre-approved, e.g. bylisting the other mobile device in its address book.

In an alternative example to that shown in FIG. 3, a centralizedarchitecture using a server can be used, similar to that shown inFIG. 1. In such an example, each of the mobile devices 108, 302, 306,310 transmits their audio signals to a computing device such as aserver, which executes the localizer functionality and determines therelative locations.

Systems such as that shown in FIG. 3, in which mobile devices compareaudio signals can be used to generate a relative topography for a groupof users of mobile devices. For example, each mobile device can use theaudio signals to determine which other mobile devices it is in proximityto. This information can be collated and used to generate an overalltopography for all users, indicating who is near who.

Note that in further examples, combinations of fixed and mobilemicrophones can also be used. For example, the examples of FIGS. 2 and 3can be combined, such that some or all rooms have fixed microphones, andthe further mobile devices also send their audio signals to the mobiledevice 108. This enables determination of both absolute and relativelocations of the mobile device 108.

Reference is now made to FIG. 4, which illustrates a flow chart of aprocess for determining a location of a mobile device using audiosignals. The process of FIG. 4 can be implemented by the localizerfunctionality 120, 204, 314 as mentioned above with reference to FIGS.1, 2 and 3, located at the mobile device 108 or computing device 118.The process of FIG. 4 may be implemented in software, hardware,firmware, or any suitable combination thereof, as described in moredetail with reference to FIG. 5 below.

The audio signal from the mobile device 108 to be localized is received402. This audio signal originates from the microphone 110 in the mobiledevice 108 as described above. The audio signal received can be in theform of digital samples of the analogue audio signal captured by themicrophone 110. The audio signals from one or more further microphonesare also received 404. These audio signals are those received from, forexample, the fixed room microphones 112, 114, 116 in the examples ofFIGS. 1 and 2, or the mobile device microphones 304, 308, 312 in theexample of FIG. 3. These audio signals can be in the form of digitalsamples of the captured analogue audio.

Optional signal processing can then be applied 406 to either or both ofthe audio signals from the mobile device 108 and the furthermicrophones. The signal processing that can be applied includes (but isnot limited to) one or more of encryption, audio fingerprinting,filtering, normalization, time-shifting, and domain transformation.

An encryption operation can be used to ensure that ambient audio signalscaptured by the microphones cannot readily be intercepted duringtransmission between elements of the localization system. In someexamples, encryption can be performed locally at the microphones, suchthat only secure audio signals are transmitted (wired or wirelessly)from the microphones.

For example, an audio fingerprinting operation can determine a“signature” for each audio signal. This is also known as content-basedaudio identification (CBID). Audio fingerprinting operations extractrepresentative features from the audio signals. The audio fingerprinttherefore characterizes the audio signal without retaining theinformation content (e.g. any captured speech) within the signal. If anaudio fingerprint operation is used, then the signatures of the audiosignals can be compared, rather than the original captured audio.Examples of features that can be extracted from audio signals in anaudio fingerprinting operation include (but are not limited to):Mel-frequency cepstrum coefficients (MFCC); spectral flatness measures(SFM); band representative vectors; and hash strings. Note that, in someexamples, the audio fingerprinting operation can be performed locally atthe microphones, to ensure that only signals without information contentare sent from the microphones.

In examples, filtering operations can be applied to one or more of theaudio signals to filter one or more frequency bands. Selecting certainfrequency bands of the audio signal to retain can be used to enhance theaudio signals by focusing the analysis on representative frequency bandsthat characterize locations. For example, a high-pass filter can be usedto remove low frequency portions of the signal that may propagate moreeasily through internal building structures, leaving higher frequencysignals that do not pass between rooms readily. In another example,band-pass filters can be used to remove frequency bands associated withhuman speech, such that mainly background noise is retained in the audiosignals.

In other examples, the filtering performed can be based on amplitude,i.e. volume level, of the audio signals. For example, only the portionsof the audio signals that are less than a selected amplitude can beretained by the filters. This enables foreground audio to be removedfrom the audio signals, and only background audio signals are retained.

In further examples, a normalization operation can be performed on theaudio signals. A normalization operation can equalize the amplitude ofthe different audio signals. For example, this can normalize the peaklevel or a mean level (e.g. RMS) of the audio signals. The normalizationcan, in other examples, also (or additionally) be performed in thefrequency domain, such that the frequency range of the audio signals isequalized.

A time-shift operation can be applied to the audio signals in yetfurther examples. The time-shift can be used to more accurately align(i.e. synchronize) the samples of the audio signals originating fromdifferent sources. For example, in the case of FIG. 1, the fixed roommicrophones are shown providing their audio signals to the computingdevice via direct wired connections. This therefore results in theseaudio signals arriving with minimal time-lag. However, the audio signalsfrom the microphone 110 in the mobile device 108 are sent over awireless link. The processing involved in coding, transmitting andsubsequently receiving and decoding the audio signals over the wirelesslink introduces a time-lag for this audio signal, relative to the othersreceived more directly. To counteract such time differences, a timeshift can be applied to one or more of the audio signals (e.g. the audiosignals from the fixed room microphones), such that the audio signalsare time-aligned.

A domain transformation operation can be applied in some examples totransform the audio signals from the time-domain to the frequencydomain. The audio signals are then subsequently compared in thefrequency domain rather than time domain. By processing the audiosignals in the frequency domain, information such as speech in the audiosignals is not directly derivable. A transformation from the time-domainto the frequency-domain can be performed using, for example, a fastFourier transform.

Note that some or all of these signal processing operations can also beperformed locally at the microphones, as well as at the localizerfunctionality.

Following the optional signal processing operations, the various audiosignals are compared. To do this, a correlation between the audio signalfrom the mobile device 108 and the audio signals from each of thefurther microphones is computed 408. In one example, the correlationcalculation can be in the form of a cross-correlation calculation. Forexample, the cross-correlation between two functions (e.g. audiosignals), f and g, can be found using the following definition:

${\left( {f\; å\; g} \right)\lbrack n\rbrack}\overset{def}{=}{\sum\limits_{m = {- \infty}}^{\infty}\;{{f^{*}\lbrack m\rbrack}{g\left\lbrack {n + m} \right\rbrack}}}$

Where n is a time lag between the two functions, and f* is the complexconjugate of f.

The output of the correlation calculations is a set of values thatindicates the degree of similarity between the audio signal from themobile device 108 and the audio signals from each of the furthermicrophones. The set of correlation values are then compared todetermine 410 which of the further microphones the mobile device is inproximity to. This can be achieved by selecting the further microphoneproviding the audio signal that has the highest degree of correlationwith the audio signal from the mobile device. In a further example, athreshold correlation value can also be set, such that the mobile deviceis determined to be in proximity to one or more further microphones forwhich the degree of correlation exceeds the threshold.

Even in an example where multiple further microphones are present in asingle room, the correlation will be greatest for the further microphonethat is closest to the mobile device. This is because the ambient noisecan vary even within the confines of a single room. Therefore, thistechnique can also be used to provide localization within a single roomenvironment.

The determined location in terms of a relative proximity to one of thefurther microphones can then be output from the localizer functionalityand utilized in any suitable location based services. As noted above,the output location can be transformed into an absolute location in thecase of fixed microphones, as the location (e.g. in terms of rooms) ofthe fixed microphones is known. Alternatively, the output location canbe in the form of a relative location in the case of mobile microphones,for example in terms of a proximity to one or more other mobile devices.

Reference is now made to FIG. 5, which illustrates a functional blockdiagram of the localizer functionality implementing the flowchart ofFIG. 4. FIG. 5 is illustrated in the context of the example system ofFIGS. 1 and 2, with fixed microphones. Note that a similar structurealso applies when the audio signals are received from other mobiledevices rather than the fixed microphones (e.g. in the case of FIG. 3).

FIG. 5 shows an audio signal 502 captured by the mobile device 108. FIG.5 also shows an audio signal 504 captured by the microphone 112 in thefirst room 102, an audio signal 506 captured by the microphone 114 inthe second room 104, and an audio signal 508 captured by the microphone116 in the first room 106.

Each of the audio signals 502, 504, 506, 508 can be in the form ofdigital samples of ambient sounds from a short period of time. In someexamples, the time period over which the sound is sampled can besufficiently short that no significant information content can beobtained from any speech that is captured by the microphones.

The audio signals 502, 504, 506, 508 are then each provided to optionalsignal processing blocks 510, which can apply one or more of the signalprocessing operations described above. These include (but are notlimited to) an audio fingerprint operation 512, a time-shift operation514, a normalize operation 516, a filter operation 518, a domaintransform operation 520, and an encryption operation 521.

Following signal processing (if applied), each audio signal 504, 506,508 from the rooms are separately applied to one input of a correlator522. The audio signal 502 from the mobile device 108 is applied to theother input of each correlator 522. The correlator 522 outputs thecorrelation between the signals applied at its inputs. The output fromeach correlator 522 is provided to a selector 524. The selector 524compares the correlation between the mobile device audio signal 502 andeach of the room audio signals 504, 506, 508, and outputs the roomhaving the highest degree of correlation as the location for the mobiledevice 108.

Reference is now made to FIG. 6, which illustrates various components ofan exemplary computing device 600 which may be implemented as any formof a computing and/or electronic device, and in which embodiments of theindoor localization technique may be implemented. For example, thecomputing device 600 of FIG. 6 can be the centralized computing device118 of FIG. 1, or the mobile device 108 of FIG. 2 or 3.

Computing device 600 comprises one or more processors 602 which may bemicroprocessors, controllers or any other suitable type of processorsfor processing computing executable instructions to control theoperation of the device in order to perform indoor localization. In someexamples, for example where a system on a chip architecture is used, theprocessors 602 may include one or more fixed function blocks (alsoreferred to as accelerators) which implement a part of the indoorlocalization methods in hardware (rather than software or firmware).

The computing device 600 comprises a communication interface 604, whichis arranged to communicate with one or more communication networks. Forexample, the communication interface can be a wireless communicationinterface arranged to communicate wirelessly with one or more mobiledevices or microphones (e.g. as shown in FIG. 1-3). The communicationinterface may also communicate with one or more wired communicationnetworks (e.g. the internet).

The computing device 600 also comprises an input interface 606 arrangedto receive input from one or more devices or data sources, such as themicrophones 112, 114, 116 as shown in of FIG. 1. An output interface 608may also optionally be provided and arranged to provide output to, forexample, a storage device or display system integral with or incommunication with the computing device. The display system may providea graphical user interface, or other user interface of any suitable typealthough this is not essential.

The computer executable instructions may be provided using anycomputer-readable media that is accessible by computing device 600.Computer-readable media may include, for example, computer storage mediasuch as memory 610 and communications media. Computer storage media,such as memory 610, includes volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, RAM, ROM, EPROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other non-transmission mediumthat can be used to store information for access by a computing device.In contrast, communication media may embody computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave, or other transportmechanism. As defined herein, computer storage media does not includecommunication media. Although the computer storage media (memory 610) isshown within the computing device 600 it will be appreciated that thestorage may be distributed or located remotely and accessed via anetwork or other communication link (e.g. using communication interface604).

Platform software comprising an operating system 612 or any othersuitable platform software may be provided at the computing device toenable application software 614 to be executed on the device. The memory610 can store executable instructions to implement the functionality ofa correlator 816 for comparing audio signals, selection logic 618 forcomparing correlation values and determining a location, and optionalsignal processing logic 620 for implementing the signal processingoperations described above. The memory 610 can also provide a data store622, which can be used to provide storage for data used by theprocessors 602 when performing the indoor localization techniques.

The term ‘computer’ is used herein to refer to any device withprocessing capability such that it can execute instructions. Thoseskilled in the art will realize that such processing capabilities areincorporated into many different devices and therefore the term‘computer’ includes PCs, servers, mobile telephones, personal digitalassistants and many other devices.

The methods described herein may be performed by software in machinereadable form on a tangible storage medium e.g. in the form of acomputer program comprising computer program code means adapted toperform all the steps of any of the methods described herein when theprogram is run on a computer and where the computer program may beembodied on a computer readable medium. Examples of tangible (ornon-transitory) storage media include disks, thumb drives, memory etcand do not include propagated signals. The software can be suitable forexecution on a parallel processor or a serial processor such that themethod steps may be carried out in any suitable order, orsimultaneously.

This acknowledges that software can be a valuable, separately tradablecommodity. It is intended to encompass software, which runs on orcontrols “dumb” or standard hardware, to carry out the desiredfunctions. It is also intended to encompass software which “describes”or defines the configuration of hardware, such as HDL (hardwaredescription language) software, as is used for designing silicon chips,or for configuring universal programmable chips, to carry out desiredfunctions.

Those skilled in the art will realize that storage devices utilized tostore program instructions can be distributed across a network. Forexample, a remote computer may store an example of the process describedas software. A local or terminal computer may access the remote computerand download a part or all of the software to run the program.Alternatively, the local computer may download pieces of the software asneeded, or execute some software instructions at the local terminal andsome at the remote computer (or computer network). Those skilled in theart will also realize that by utilizing conventional techniques known tothose skilled in the art that all, or a portion of the softwareinstructions may be carried out by a dedicated circuit, such as a DSP,programmable logic array, or the like.

Any range or device value given herein may be extended or alteredwithout losing the effect sought, as will be apparent to the skilledperson.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

It will be understood that the benefits and advantages described abovemay relate to one embodiment or may relate to several embodiments. Theembodiments are not limited to those that solve any or all of the statedproblems or those that have any or all of the stated benefits andadvantages. It will further be understood that reference to ‘an’ itemrefers to one or more of those items.

The steps of the methods described herein may be carried out in anysuitable order, or simultaneously where appropriate. Additionally,individual blocks may be deleted from any of the methods withoutdeparting from the spirit and scope of the subject matter describedherein. Aspects of any of the examples described above may be combinedwith aspects of any of the other examples described to form furtherexamples without losing the effect sought.

The term ‘comprising’ is used herein to mean including the method blocksor elements identified, but that such blocks or elements do not comprisean exclusive list and a method or apparatus may contain additionalblocks or elements.

It will be understood that the above description of a preferredembodiment is given by way of example only and that variousmodifications may be made by those skilled in the art. The abovespecification, examples and data provide a complete description of thestructure and use of exemplary embodiments of the invention. Althoughvarious embodiments of the invention have been described above with acertain degree of particularity, or with reference to one or moreindividual embodiments, those skilled in the art could make numerousalterations to the disclosed embodiments without departing from thespirit or scope of this invention.

The invention claimed is:
 1. A computer-implemented method of localizinga mobile device, comprising: receiving, at a processor, a first audiosignal captured by a microphone located at the mobile device; receiving,at the processor, a second audio signal captured from a furthermicrophone; computing a correlation value for the first audio signal andsecond audio signal; and determining whether the mobile device is inproximity to the further microphone using the correlation value.
 2. Amethod according claim 1, wherein the step of computing the correlationvalue comprises calculating a cross-correlation between the first audiosignal and second audio signal.
 3. A method according to claim 1,wherein the first audio signal comprises ambient noise from the vicinityof the mobile device, and the second audio signal comprises ambientnoise from the vicinity of the further microphone.
 4. A method accordingto claim 1, wherein the further microphone is non-mobile and isassociated with a predefined location.
 5. A method according to claim 4,wherein the predefined location is a room of an indoor environment.
 6. Amethod according to claim 4, wherein the processor is located at aserver, and wherein the step of receiving the first audio signalcomprises receiving the first audio signal via a wireless interface, andthe step of receiving the second audio signal comprises receiving thesecond audio signal via and analog-to-digital converter.
 7. A methodaccording to claim 4, wherein the processor is located at the mobiledevice, and wherein the step of receiving the second audio signalcomprises receiving the second audio signal via a wireless communicationinterface at the mobile device.
 8. A method according to claim 1,wherein the processor is located at the mobile device, and the furthermicrophone is located in a further mobile device, such that the step ofdetermining determines whether the mobile device is in proximity to thefurther mobile device.
 9. A method according to claim 1, furthercomprising the steps of: receiving, at the processor, a third audiosignal captured from an additional microphone; and computing a furthercorrelation value for the first audio signal and third audio signal. 10.A method according to claim 9, further comprising the step of comparingthe correlation value and the further correlation value, and wherein thestep of determining comprises determining that the mobile device is inproximity to the further microphone if the second audio signal has ahigher degree of correlation with the first audio signal than the thirdaudio signal.
 11. A method according to claim 1, further comprising thestep of determining an audio fingerprint for the first and second audiosignals prior to computing the correlation.
 12. A method according toclaim 1, further comprising the step of at least one of normalizing andfiltering at least one of the first and second audio signals prior tocomputing the correlation.
 13. A method according to claim 1, furthercomprising the step of transforming the first and second audio signalsinto frequency domain signals prior to computing the correlation.
 14. Amethod according to claim 1, further comprising the step of applying atime-shift to at least one of the first and second audio signals priorto computing the correlation.
 15. A mobile device, comprising: amicrophone arranged to capture a first audio signal from the vicinity ofthe mobile device; a communication interface arranged to receive asecond audio signal captured from a further microphone; a processorconnected to the microphone and the communication interface, andarranged to compute a correlation value for the first audio signal andsecond audio signal, and determine whether the mobile device is inproximity to the further microphone using the correlation value.
 16. Amobile device according to claim 15, wherein the further microphone islocated at a further mobile device, and the processor is arranged todetermine whether the mobile device is in proximity to the furthermobile device.
 17. A method according to claim 1, wherein the furthermicrophone is non-mobile and is associated with a predefined location,and the processor is arranged to determine whether the mobile device isin proximity to the predefined location.
 18. A mobile device accordingto claim 15, wherein the mobile device is a mobile telephone or laptopcomputer.
 19. An indoor positioning system, comprising: a plurality offixed microphones, each located in a different room of an indoorenvironment, and each arranged to capture audio signals from itsrespective room; a wireless interface arranged to receive an audiosignal from a mobile device having a microphone arranged to capture theaudio signal from the vicinity of the mobile device; and a computingdevice connected to the plurality of fixed microphones and the wirelessinterface, and arranged to receive each of the fixed microphone audiosignals and the mobile device audio signal, compute a correlationbetween the mobile device audio signal and each of the fixed microphoneaudio signals, determine a selected fixed microphone providing the audiosignal having the highest degree of correlation with the mobile deviceaudio signal, and outputting the room associated with the selected fixedmicrophone as the mobile device location.
 20. An indoor positioningsystem according to claim 19, wherein at least one of the plurality offixed microphones is located in a landline telephone or telephoneconferencing device.